Overview

Dataset statistics

Number of variables15
Number of observations19611
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory120.0 B

Variable types

Numeric15

Alerts

時間(min) is highly correlated with S and 13 other fieldsHigh correlation
S is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T1 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T2 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T3 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T4 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T5 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T6 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T7 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T8 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T9 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T10 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T11 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T12 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
Z is highly correlated with 時間(min) and 13 other fieldsHigh correlation
時間(min) is uniformly distributed Uniform
時間(min) has unique values Unique
S has 457 (2.3%) zeros Zeros

Reproduction

Analysis started2022-11-11 03:24:56.259370
Analysis finished2022-11-11 03:25:11.095252
Duration14.84 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

時間(min)
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct19611
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean817.0866667
Minimum0.003333333333
Maximum1634.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.3 KiB
2022-11-11T11:25:11.132128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.003333333333
5-th percentile81.71166667
Q1408.545
median817.0866667
Q31225.628333
95-th percentile1552.461667
Maximum1634.17
Range1634.166667
Interquartile range (IQR)817.0833333

Descriptive statistics

Standard deviation471.7793667
Coefficient of variation (CV)0.5773920759
Kurtosis-1.2
Mean817.0866667
Median Absolute Deviation (MAD)408.5833333
Skewness5.928706166 × 10-17
Sum16023886.62
Variance222575.7708
MonotonicityStrictly increasing
2022-11-11T11:25:11.192309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0033333333331
 
< 0.1%
1089.2533331
 
< 0.1%
1089.8366671
 
< 0.1%
1089.7533331
 
< 0.1%
1089.671
 
< 0.1%
1089.5866671
 
< 0.1%
1089.5033331
 
< 0.1%
1089.421
 
< 0.1%
1089.3366671
 
< 0.1%
1089.171
 
< 0.1%
Other values (19601)19601
99.9%
ValueCountFrequency (%)
0.0033333333331
< 0.1%
0.086666666671
< 0.1%
0.171
< 0.1%
0.25333333331
< 0.1%
0.33666666671
< 0.1%
0.421
< 0.1%
0.50333333331
< 0.1%
0.58666666671
< 0.1%
0.671
< 0.1%
0.75333333331
< 0.1%
ValueCountFrequency (%)
1634.171
< 0.1%
1634.0866671
< 0.1%
1634.0033331
< 0.1%
1633.921
< 0.1%
1633.8366671
< 0.1%
1633.7533331
< 0.1%
1633.671
< 0.1%
1633.5866671
< 0.1%
1633.5033331
< 0.1%
1633.421
< 0.1%

S
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12934.34312
Minimum0
Maximum20001
Zeros457
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size153.3 KiB
2022-11-11T11:25:11.250646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7999
Q18000
median13999
Q314000
95-th percentile20000
Maximum20001
Range20001
Interquartile range (IQR)6000

Descriptive statistics

Standard deviation5042.97138
Coefficient of variation (CV)0.3898900263
Kurtosis-0.7039820629
Mean12934.34312
Median Absolute Deviation (MAD)5999
Skewness-0.0686361784
Sum253655403
Variance25431560.34
MonotonicityNot monotonic
2022-11-11T11:25:11.299481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
139995689
29.0%
80004261
21.7%
200003824
19.5%
79992903
14.8%
140001510
 
7.7%
19999947
 
4.8%
0457
 
2.3%
200012
 
< 0.1%
41
 
< 0.1%
68001
 
< 0.1%
Other values (16)16
 
0.1%
ValueCountFrequency (%)
0457
2.3%
11
 
< 0.1%
41
 
< 0.1%
131
 
< 0.1%
571
 
< 0.1%
11301
 
< 0.1%
16931
 
< 0.1%
29601
 
< 0.1%
42941
 
< 0.1%
50331
 
< 0.1%
ValueCountFrequency (%)
200012
 
< 0.1%
200003824
19.5%
19999947
 
4.8%
163171
 
< 0.1%
140001510
 
7.7%
139995689
29.0%
138961
 
< 0.1%
134661
 
< 0.1%
122521
 
< 0.1%
109251
 
< 0.1%

T1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct43
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.02946816
Minimum23.5
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.3 KiB
2022-11-11T11:25:11.355581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.5
5-th percentile24.5
Q125
median26.2
Q326.6
95-th percentile27.6
Maximum27.7
Range4.2
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.100766048
Coefficient of variation (CV)0.04228922548
Kurtosis-1.109058248
Mean26.02946816
Median Absolute Deviation (MAD)1.2
Skewness-0.03320757913
Sum510463.9
Variance1.211685893
MonotonicityNot monotonic
2022-11-11T11:25:11.408651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
26.42100
 
10.7%
27.61699
 
8.7%
251562
 
8.0%
26.51414
 
7.2%
27.51176
 
6.0%
261054
 
5.4%
26.2987
 
5.0%
27.4965
 
4.9%
24.6845
 
4.3%
24.9843
 
4.3%
Other values (33)6966
35.5%
ValueCountFrequency (%)
23.5163
0.8%
23.636
 
0.2%
23.734
 
0.2%
23.823
 
0.1%
23.938
 
0.2%
2446
 
0.2%
24.158
 
0.3%
24.280
 
0.4%
24.3118
 
0.6%
24.4362
1.8%
ValueCountFrequency (%)
27.7754
3.8%
27.61699
8.7%
27.51176
6.0%
27.4965
4.9%
27.337
 
0.2%
27.257
 
0.3%
27.122
 
0.1%
2720
 
0.1%
26.915
 
0.1%
26.83
 
< 0.1%

T2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.63638264
Minimum23.5
Maximum25.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.3 KiB
2022-11-11T11:25:11.458484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.5
5-th percentile23.7
Q124.2
median24.8
Q325.1
95-th percentile25.2
Maximum25.5
Range2
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.4888325019
Coefficient of variation (CV)0.01984189436
Kurtosis-0.9048170665
Mean24.63638264
Median Absolute Deviation (MAD)0.3
Skewness-0.5985113475
Sum483144.1
Variance0.2389572149
MonotonicityNot monotonic
2022-11-11T11:25:11.505630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
25.13347
17.1%
253031
15.5%
25.21727
8.8%
24.21545
 
7.9%
24.91169
 
6.0%
24.51081
 
5.5%
24.6963
 
4.9%
24869
 
4.4%
24.8856
 
4.4%
24.1850
 
4.3%
Other values (11)4173
21.3%
ValueCountFrequency (%)
23.5299
 
1.5%
23.6323
 
1.6%
23.7446
 
2.3%
23.8475
 
2.4%
23.9669
3.4%
24869
4.4%
24.1850
4.3%
24.21545
7.9%
24.3803
4.1%
24.4392
 
2.0%
ValueCountFrequency (%)
25.515
 
0.1%
25.411
 
0.1%
25.3111
 
0.6%
25.21727
8.8%
25.13347
17.1%
253031
15.5%
24.91169
 
6.0%
24.8856
 
4.4%
24.7629
 
3.2%
24.6963
 
4.9%

T3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.54745296
Minimum23.5
Maximum25.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.3 KiB
2022-11-11T11:25:11.596324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.5
5-th percentile23.7
Q124.2
median24.7
Q324.9
95-th percentile25.1
Maximum25.4
Range1.9
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.4337789944
Coefficient of variation (CV)0.01767103883
Kurtosis-0.7029634541
Mean24.54745296
Median Absolute Deviation (MAD)0.3
Skewness-0.6560876358
Sum481400.1
Variance0.188164216
MonotonicityNot monotonic
2022-11-11T11:25:11.641060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
24.93927
20.0%
252283
11.6%
24.71743
8.9%
24.21716
8.8%
24.81476
 
7.5%
24.51270
 
6.5%
25.1922
 
4.7%
24.1899
 
4.6%
24816
 
4.2%
24.4804
 
4.1%
Other values (10)3755
19.1%
ValueCountFrequency (%)
23.5256
 
1.3%
23.6426
 
2.2%
23.7451
 
2.3%
23.8672
 
3.4%
23.9521
 
2.7%
24816
4.2%
24.1899
4.6%
24.21716
8.8%
24.3612
 
3.1%
24.4804
4.1%
ValueCountFrequency (%)
25.415
 
0.1%
25.34
 
< 0.1%
25.276
 
0.4%
25.1922
 
4.7%
252283
11.6%
24.93927
20.0%
24.81476
 
7.5%
24.71743
8.9%
24.6722
 
3.7%
24.51270
 
6.5%

T4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.23310387
Minimum23.6
Maximum26.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.3 KiB
2022-11-11T11:25:11.692284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.6
5-th percentile23.9
Q124.5
median25.4
Q325.8
95-th percentile26.4
Maximum26.4
Range2.8
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation0.8143088336
Coefficient of variation (CV)0.03227144935
Kurtosis-1.169670737
Mean25.23310387
Median Absolute Deviation (MAD)0.8
Skewness-0.1799672123
Sum494846.4
Variance0.6630988765
MonotonicityNot monotonic
2022-11-11T11:25:11.740046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
26.31976
 
10.1%
25.61566
 
8.0%
24.51404
 
7.2%
25.71292
 
6.6%
26.21191
 
6.1%
26.41149
 
5.9%
24.6948
 
4.8%
25.4848
 
4.3%
24.4819
 
4.2%
25.8796
 
4.1%
Other values (19)7622
38.9%
ValueCountFrequency (%)
23.6318
 
1.6%
23.7266
 
1.4%
23.8268
 
1.4%
23.9318
 
1.6%
24358
 
1.8%
24.1469
 
2.4%
24.2544
 
2.8%
24.3738
3.8%
24.4819
4.2%
24.51404
7.2%
ValueCountFrequency (%)
26.41149
5.9%
26.31976
10.1%
26.21191
6.1%
26.1281
 
1.4%
26113
 
0.6%
25.978
 
0.4%
25.8796
4.1%
25.71292
6.6%
25.61566
8.0%
25.5646
 
3.3%

T5
Real number (ℝ≥0)

HIGH CORRELATION

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.1863648
Minimum23.8
Maximum26.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.3 KiB
2022-11-11T11:25:11.790053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.8
5-th percentile23.9
Q124.6
median25.3
Q325.8
95-th percentile26.1
Maximum26.1
Range2.3
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.7054155612
Coefficient of variation (CV)0.02800783547
Kurtosis-1.142049748
Mean25.1863648
Median Absolute Deviation (MAD)0.7
Skewness-0.2792184268
Sum493929.8
Variance0.497611114
MonotonicityNot monotonic
2022-11-11T11:25:11.836633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
26.12943
 
15.0%
24.61654
 
8.4%
25.71314
 
6.7%
261285
 
6.6%
25.61058
 
5.4%
25.5912
 
4.7%
24.7848
 
4.3%
24.5770
 
3.9%
25.4746
 
3.8%
25.3722
 
3.7%
Other values (14)7359
37.5%
ValueCountFrequency (%)
23.8622
 
3.2%
23.9430
 
2.2%
24351
 
1.8%
24.1362
 
1.8%
24.2425
 
2.2%
24.3587
 
3.0%
24.4546
 
2.8%
24.5770
3.9%
24.61654
8.4%
24.7848
4.3%
ValueCountFrequency (%)
26.12943
15.0%
261285
6.6%
25.9360
 
1.8%
25.8703
 
3.6%
25.71314
6.7%
25.61058
 
5.4%
25.5912
 
4.7%
25.4746
 
3.8%
25.3722
 
3.7%
25.2657
 
3.4%

T6
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.47364744
Minimum24.2
Maximum26.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.3 KiB
2022-11-11T11:25:11.882439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.2
5-th percentile24.3
Q124.9
median25.6
Q326.1
95-th percentile26.3
Maximum26.3
Range2.1
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.6571344232
Coefficient of variation (CV)0.02579663649
Kurtosis-1.289570134
Mean25.47364744
Median Absolute Deviation (MAD)0.6
Skewness-0.3143363023
Sum499563.7
Variance0.4318256501
MonotonicityNot monotonic
2022-11-11T11:25:11.924298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
26.22586
 
13.2%
26.31818
 
9.3%
26.11398
 
7.1%
24.91201
 
6.1%
24.31141
 
5.8%
261044
 
5.3%
24.8987
 
5.0%
25.8943
 
4.8%
25.9863
 
4.4%
25798
 
4.1%
Other values (12)6832
34.8%
ValueCountFrequency (%)
24.24
 
< 0.1%
24.31141
5.8%
24.4669
3.4%
24.5279
 
1.4%
24.6767
3.9%
24.7549
2.8%
24.8987
5.0%
24.91201
6.1%
25798
4.1%
25.1726
3.7%
ValueCountFrequency (%)
26.31818
9.3%
26.22586
13.2%
26.11398
7.1%
261044
5.3%
25.9863
 
4.4%
25.8943
 
4.8%
25.7731
 
3.7%
25.6660
 
3.4%
25.5665
 
3.4%
25.4547
 
2.8%

T7
Real number (ℝ≥0)

HIGH CORRELATION

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.44449034
Minimum23.5
Maximum25.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.3 KiB
2022-11-11T11:25:11.967153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.5
5-th percentile23.6
Q124.1
median24.6
Q324.8
95-th percentile25
Maximum25.2
Range1.7
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.4218057017
Coefficient of variation (CV)0.0172556554
Kurtosis-0.7745611264
Mean24.44449034
Median Absolute Deviation (MAD)0.3
Skewness-0.6141192112
Sum479380.9
Variance0.17792005
MonotonicityNot monotonic
2022-11-11T11:25:12.012372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
24.83925
20.0%
24.61969
10.0%
24.91818
9.3%
24.11723
8.8%
24.71455
 
7.4%
24.41314
 
6.7%
251029
 
5.2%
24935
 
4.8%
24.5902
 
4.6%
23.9824
 
4.2%
Other values (8)3717
19.0%
ValueCountFrequency (%)
23.5545
 
2.8%
23.6554
 
2.8%
23.7534
 
2.7%
23.8626
 
3.2%
23.9824
4.2%
24935
4.8%
24.11723
8.8%
24.2611
 
3.1%
24.3787
4.0%
24.41314
6.7%
ValueCountFrequency (%)
25.25
 
< 0.1%
25.155
 
0.3%
251029
 
5.2%
24.91818
9.3%
24.83925
20.0%
24.71455
 
7.4%
24.61969
10.0%
24.5902
 
4.6%
24.41314
 
6.7%
24.3787
 
4.0%

T8
Real number (ℝ≥0)

HIGH CORRELATION

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.68682372
Minimum23.5
Maximum25.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.3 KiB
2022-11-11T11:25:12.060987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.5
5-th percentile23.7
Q124.3
median24.9
Q325.1
95-th percentile25.3
Maximum25.4
Range1.9
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.5151263812
Coefficient of variation (CV)0.02086645034
Kurtosis-0.943803325
Mean24.68682372
Median Absolute Deviation (MAD)0.3
Skewness-0.5997614438
Sum484133.3
Variance0.2653551887
MonotonicityNot monotonic
2022-11-11T11:25:12.105837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
25.23389
17.3%
25.12930
14.9%
251762
 
9.0%
24.31263
 
6.4%
25.31106
 
5.6%
24.61021
 
5.2%
24.21014
 
5.2%
24.1869
 
4.4%
24841
 
4.3%
24.8750
 
3.8%
Other values (10)4666
23.8%
ValueCountFrequency (%)
23.5306
 
1.6%
23.6269
 
1.4%
23.7460
 
2.3%
23.8435
 
2.2%
23.9704
3.6%
24841
4.3%
24.1869
4.4%
24.21014
5.2%
24.31263
6.4%
24.4471
 
2.4%
ValueCountFrequency (%)
25.426
 
0.1%
25.31106
 
5.6%
25.23389
17.3%
25.12930
14.9%
251762
9.0%
24.9731
 
3.7%
24.8750
 
3.8%
24.7699
 
3.6%
24.61021
 
5.2%
24.5565
 
2.9%

T9
Real number (ℝ≥0)

HIGH CORRELATION

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.76356127
Minimum22.6
Maximum24.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.3 KiB
2022-11-11T11:25:12.156691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.6
5-th percentile23.1
Q123.4
median23.9
Q324.1
95-th percentile24.5
Maximum24.8
Range2.2
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.4326607834
Coefficient of variation (CV)0.01820689999
Kurtosis-0.6702045058
Mean23.76356127
Median Absolute Deviation (MAD)0.3
Skewness-0.2855967931
Sum466027.2
Variance0.1871953534
MonotonicityNot monotonic
2022-11-11T11:25:12.240382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
242623
13.4%
23.92438
12.4%
24.12020
10.3%
23.21833
9.3%
23.81716
8.8%
24.21462
7.5%
23.41430
7.3%
23.31284
 
6.5%
23.1769
 
3.9%
23.7716
 
3.7%
Other values (13)3320
16.9%
ValueCountFrequency (%)
22.698
 
0.5%
22.766
 
0.3%
22.859
 
0.3%
22.9270
 
1.4%
23328
 
1.7%
23.1769
3.9%
23.21833
9.3%
23.31284
6.5%
23.41430
7.3%
23.5259
 
1.3%
ValueCountFrequency (%)
24.810
 
0.1%
24.7142
 
0.7%
24.6334
 
1.7%
24.5507
 
2.6%
24.4222
 
1.1%
24.3563
 
2.9%
24.21462
7.5%
24.12020
10.3%
242623
13.4%
23.92438
12.4%

T10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.4242364
Minimum23.3
Maximum25.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.3 KiB
2022-11-11T11:25:12.285196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.3
5-th percentile23.8
Q124
median24.6
Q324.8
95-th percentile25
Maximum25.2
Range1.9
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.4125226457
Coefficient of variation (CV)0.01688988916
Kurtosis-0.9270305946
Mean24.4242364
Median Absolute Deviation (MAD)0.2
Skewness-0.5371372756
Sum478983.7
Variance0.1701749332
MonotonicityNot monotonic
2022-11-11T11:25:12.331424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
24.83542
18.1%
24.72970
15.1%
24.62228
11.4%
241952
10.0%
23.91693
8.6%
24.51099
 
5.6%
23.81021
 
5.2%
24.4900
 
4.6%
25825
 
4.2%
24.9718
 
3.7%
Other values (10)2663
13.6%
ValueCountFrequency (%)
23.3115
 
0.6%
23.481
 
0.4%
23.553
 
0.3%
23.6296
 
1.5%
23.7431
 
2.2%
23.81021
5.2%
23.91693
8.6%
241952
10.0%
24.1712
 
3.6%
24.2389
 
2.0%
ValueCountFrequency (%)
25.28
 
< 0.1%
25.1168
 
0.9%
25825
 
4.2%
24.9718
 
3.7%
24.83542
18.1%
24.72970
15.1%
24.62228
11.4%
24.51099
 
5.6%
24.4900
 
4.6%
24.3410
 
2.1%

T11
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.83672429
Minimum23.1
Maximum25.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.3 KiB
2022-11-11T11:25:12.381660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.1
5-th percentile24
Q124.5
median24.9
Q325.1
95-th percentile25.5
Maximum25.8
Range2.7
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.4422746803
Coefficient of variation (CV)0.01780728711
Kurtosis1.108958695
Mean24.83672429
Median Absolute Deviation (MAD)0.3
Skewness-0.7295315925
Sum487073
Variance0.1956068928
MonotonicityNot monotonic
2022-11-11T11:25:12.430735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
25.12374
12.1%
252134
10.9%
25.21798
9.2%
24.91653
8.4%
24.71624
8.3%
24.51615
8.2%
24.41192
 
6.1%
25.31175
 
6.0%
24.61095
 
5.6%
24.81003
 
5.1%
Other values (18)3948
20.1%
ValueCountFrequency (%)
23.19
 
< 0.1%
23.2113
 
0.6%
23.398
 
0.5%
23.429
 
0.1%
23.519
 
0.1%
23.636
 
0.2%
23.761
 
0.3%
23.845
 
0.2%
23.9349
1.8%
24303
1.5%
ValueCountFrequency (%)
25.882
 
0.4%
25.7377
 
1.9%
25.6260
 
1.3%
25.5271
 
1.4%
25.4687
 
3.5%
25.31175
6.0%
25.21798
9.2%
25.12374
12.1%
252134
10.9%
24.91653
8.4%

T12
Real number (ℝ≥0)

HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.64543878
Minimum22.3
Maximum24.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.3 KiB
2022-11-11T11:25:12.477234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.3
5-th percentile22.8
Q123
median23.7
Q324.2
95-th percentile24.5
Maximum24.8
Range2.5
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.5878282443
Coefficient of variation (CV)0.02486011149
Kurtosis-1.15788277
Mean23.64543878
Median Absolute Deviation (MAD)0.5
Skewness-0.2038823145
Sum463710.7
Variance0.3455420448
MonotonicityNot monotonic
2022-11-11T11:25:12.525929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
24.21775
 
9.1%
23.71675
 
8.5%
24.31580
 
8.1%
22.91553
 
7.9%
231550
 
7.9%
24.11531
 
7.8%
241116
 
5.7%
22.81008
 
5.1%
23.5922
 
4.7%
23.8826
 
4.2%
Other values (16)6075
31.0%
ValueCountFrequency (%)
22.374
 
0.4%
22.483
 
0.4%
22.551
 
0.3%
22.6276
 
1.4%
22.7495
 
2.5%
22.81008
5.1%
22.91553
7.9%
231550
7.9%
23.1795
4.1%
23.2390
 
2.0%
ValueCountFrequency (%)
24.875
 
0.4%
24.7221
 
1.1%
24.6459
 
2.3%
24.5414
 
2.1%
24.4466
 
2.4%
24.31580
8.1%
24.21775
9.1%
24.11531
7.8%
241116
5.7%
23.9796
4.1%

Z
Real number (ℝ)

HIGH CORRELATION

Distinct188
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.46827291
Minimum-1.2
Maximum65.3
Zeros35
Zeros (%)0.2%
Negative100
Negative (%)0.5%
Memory size153.3 KiB
2022-11-11T11:25:12.580070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.2
5-th percentile29.3
Q131.4
median41.5
Q344.6
95-th percentile63.5
Maximum65.3
Range66.5
Interquartile range (IQR)13.2

Descriptive statistics

Standard deviation13.26027444
Coefficient of variation (CV)0.3122395503
Kurtosis-0.2719978243
Mean42.46827291
Median Absolute Deviation (MAD)10.1
Skewness0.2876020169
Sum832845.3
Variance175.8348781
MonotonicityNot monotonic
2022-11-11T11:25:12.636352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.51557
 
7.9%
42.71243
 
6.3%
30.81194
 
6.1%
31.11130
 
5.8%
63.21052
 
5.4%
31.81039
 
5.3%
42.41023
 
5.2%
41.8980
 
5.0%
63.5902
 
4.6%
31.4876
 
4.5%
Other values (178)8615
43.9%
ValueCountFrequency (%)
-1.21
 
< 0.1%
-0.96
 
< 0.1%
-0.630
0.2%
-0.363
0.3%
035
0.2%
0.37
 
< 0.1%
0.62
 
< 0.1%
4.91
 
< 0.1%
5.52
 
< 0.1%
5.81
 
< 0.1%
ValueCountFrequency (%)
65.31
 
< 0.1%
6514
 
0.1%
64.779
 
0.4%
64.4197
 
1.0%
64.1169
 
0.9%
63.8344
 
1.8%
63.5902
4.6%
63.21052
5.4%
62.9533
2.7%
62.6634
3.2%

Interactions

2022-11-11T11:25:10.112040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:58.947567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.814873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.643907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.349977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.181681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.006436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.805264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.573265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.327612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.145841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.960505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.729693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.559764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.340749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.162967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.008438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.872679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.695115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.404929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.236643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.059118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.856618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.622402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.381431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.200090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.011893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.784222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.612221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.393959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.213549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.063706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.928490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.745703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.506659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.339297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.110943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.907414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.675224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.438240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.253754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.062721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.839311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.665602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.444876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.258474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.111737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.975142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.789555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.554976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.387250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.204435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.952264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.722457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.488072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.301427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.106580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.887238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.712444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.489514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.310594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.169544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.031246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.840419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.609810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.441361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.260364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.049934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.772809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.542887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.358986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.157409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.944987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.764208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.540003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.364417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.227348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.086061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.889247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.665221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.495179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.312227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.099766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.821633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.596731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.412136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.209234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.000798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.815835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.590921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.415333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.283159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.140876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.936089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.719286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.547122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.362060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.148048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.913903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.648647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.463962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.259066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.053651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.864612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.640207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.461780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.332727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.189943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.979995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.768089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.595426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.408871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.192873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.957649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.697483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.513160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.305908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.103513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.911453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.686052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.507331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.384496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.237320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.022349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.816163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.642268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.455210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.236725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.000032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.787218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.560490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.349526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.151352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.956678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.730976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.557840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.442310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.292136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.071232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.869563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.695170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.507035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.286557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.048920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.841003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.655349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.399306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.206089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.007581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.780217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.609736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.499119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.346363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.120027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.924378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.748623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.559359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.337108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.097175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.893825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.710157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.491994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.260847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.058487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.830322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.656579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.548951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.395198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.164057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.974292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.797647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.606807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.382044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.140324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.941664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.758719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.537866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.353049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.104865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.876154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.710446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.607753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.451119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.214209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.029263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.852462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.659404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.433722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.191153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.996511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.812537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.588539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.408860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.199546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.926911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.757865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.663374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.502765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.260052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.081088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.905775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.709321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.480564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.237057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.046112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.862369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.635310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.459689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.247475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.974005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.804707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:24:59.715199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:00.552696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:01.303873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.129997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:02.955607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:03.756592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:04.526423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:05.282167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.094864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:06.910329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:07.681221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:08.509521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:09.292910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:10.064116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-11T11:25:12.694157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-11T11:25:12.769307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T11:25:12.887961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T11:25:12.968940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T11:25:13.050178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T11:25:10.928156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T11:25:11.040467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

時間(min)ST1T2T3T4T5T6T7T8T9T10T11T12Z
00.003333023.523.623.623.623.824.323.623.522.623.323.122.30.0
10.086667023.523.623.623.623.824.323.623.522.623.323.122.3-0.6
20.170000023.523.523.623.623.824.323.623.522.623.323.122.30.6
30.253333023.523.523.623.623.824.323.623.522.623.323.122.3-0.6
40.336667023.523.523.623.623.824.323.623.522.623.323.122.3-0.3
50.420000023.523.523.623.623.824.323.623.522.623.323.122.30.0
60.503333023.523.523.623.623.824.323.623.522.623.323.122.3-0.3
70.586667023.523.523.623.623.824.323.623.522.623.323.122.3-0.6
80.670000023.523.523.623.623.824.323.623.522.623.323.122.3-0.3
90.753333023.523.523.623.623.824.323.623.522.623.323.222.3-0.6

Last rows

時間(min)ST1T2T3T4T5T6T7T8T9T10T11T12Z
196011633.4200002000027.424.924.726.226.026.224.525.023.924.724.723.763.5
196021633.5033332000027.424.924.726.226.026.224.525.023.924.724.723.763.2
196031633.5866672000027.424.924.726.226.026.224.525.023.924.724.723.763.2
196041633.6700002000027.424.924.726.226.026.224.525.023.924.724.723.763.5
196051633.7533332000027.424.924.726.226.026.224.525.023.924.724.723.763.2
196061633.8366672000027.424.924.726.226.026.224.625.023.924.724.723.762.9
196071633.9200002000027.424.924.726.226.026.224.625.023.924.724.723.762.9
196081634.0033332000027.424.924.726.226.026.224.625.023.924.724.723.763.5
196091634.0866672000027.424.924.726.226.026.224.625.023.924.724.723.763.8
196101634.1700002000027.424.924.726.226.026.224.625.023.924.724.723.763.2